Reconfigurable hyperbolic polaritonics with correlated oxide metasurfaces.

Polaritons enable subwavelength confinement and highly anisotropic flows of light over a wide spectral range, holding the promise for applications in modern nanophotonic and optoelectronic devices. However, to fully realize their practical application potential, facile methods enabling nanoscale active control of polaritons are needed. Here, we introduce a hybrid polaritonic-oxide heterostructure platform consisting of van der Waals crystals, such as hexagonal boron nitride (hBN) or alpha-phase molybdenum trioxide (α-MoO3), transferred on nanoscale oxygen vacancy patterns on the surface of prototypical correlated perovskite oxide, samarium nickel oxide, SmNiO3 (SNO). Using a combination of scanning probe microscopy and infrared nanoimaging techniques, we demonstrate nanoscale reconfigurability of complex hyperbolic phonon polaritons patterned at the nanoscale with high resolution. Hydrogenation and temperature modulation allow spatially localized conductivity modulation of SNO nanoscale patterns, enabling robust real-time modulation and nanoscale reconfiguration of hyperbolic polaritons. Our work paves the way towards nanoscale programmable metasurface engineering for reconfigurable nanophotonic applications.

Neuromorphic learning with Mott insulator NiO.

Habituation and sensitization (nonassociative learning) are among the most fundamental forms of learning and memory behavior present in organisms that enable adaptation and learning in dynamic environments. Emulating such features of intelligence found in nature in the solid state can serve as inspiration for algorithmic simulations in artificial neural networks and potential use in neuromorphic computing. Here, we demonstrate nonassociative learning with a prototypical Mott insulator, nickel oxide (NiO), under a variety of external stimuli at and above room temperature. Similar to biological species such as Aplysia, habituation and sensitization of NiO possess time-dependent plasticity relying on both strength and time interval between stimuli. A combination of experimental approaches and first-principles calculations reveals that such learning behavior of NiO results from dynamic modulation of its defect and electronic structure. An artificial neural network model inspired by such nonassociative learning is simulated to show advantages for an unsupervised clustering task in accuracy and reducing catastrophic interference, which could help mitigate the stability-plasticity dilemma. Mott insulators can therefore serve as building blocks to examine learning behavior noted in biology and inspire new learning algorithms for artificial intelligence.

Carrier localization in perovskite nickelates from oxygen vacancies.

Point defects, such as oxygen vacancies, control the physical properties of complex oxides, relevant in active areas of research from superconductivity to resistive memory to catalysis. In most oxide semiconductors, electrons that are associated with oxygen vacancies occupy the conduction band, leading to an increase in the electrical conductivity. Here we demonstrate, in contrast, that in the correlated-electron perovskite rare-earth nickelates, RNiO3 (R is a rare-earth element such as Sm or Nd), electrons associated with oxygen vacancies strongly localize, leading to a dramatic decrease in the electrical conductivity by several orders of magnitude. This unusual behavior is found to stem from the combination of crystal field splitting and filling-controlled Mott-Hubbard electron-electron correlations in the Ni 3d orbitals. Furthermore, we show the distribution of oxygen vacancies in NdNiO3 can be controlled via an electric field, leading to analog resistance switching behavior. This study demonstrates the potential of nickelates as testbeds to better understand emergent physics in oxide heterostructures as well as candidate systems in the emerging fields of artificial intelligence.

Hyperspectral time-domain terahertz nano-imaging.

Terahertz (THz) near-field microscopy has wide and unprecedented application potential for nanoscale materials and photonic-device characterization. Here, we introduce hyperspectral THz nano-imaging by combining scattering-type scanning near-field optical microscopy (s-SNOM) with THz time-domain spectroscopy (TDS). We describe the technical implementations that enabled this achievement and demonstrate its performance with a heterogeneously doped Si semiconductor sample. Specifically, we recorded a hyperspectral image of 40 by 20 pixels in 180 minutes and with a spatial resolution of about ~170 nm by measuring at each pixel with a time domain spectrum covering the range from 0.4 to 1.8 THz. Fitting the spectra with a Drude model allows for measuring-noninvasively and without the need for Ohmic contacts-the local mobile carrier concentration of the differently doped Si areas. We envision wide application potential for THz hyperspectral nano-imaging, including nanoscale carrier profiling of industrial semiconductor structures or characterizing complex and correlated electron matter, as well as low dimensional (1D or 2D) materials.